GLIOMATCH: The malignant Glioma immuno-oncology matchmaker: towards data-driven precision medicine using spatially resolved radio-multiomics
GLIOMATCH: The malignant Glioma immuno-oncology matchmaker: towards data-driven precision medicine using spatially resolved radio-multiomics
批准号:
10113516
负责人:
金额:
$65.57万
依托单位:
依托单位国家:
英国
项目类别:
EU-Funded
财政年份:
2024
资助国家:
英国
项目状态:
未结题
起止时间:
2024 至 --
中文摘要
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英文摘要
Adult and paediatric malignant glioma (GBM and pHGG) remain among the most difficult-to-treat cancers with 5-year survival ratesof <5% despite intensive standard-of-care therapy. The differences among patients and the heterogeneous and plastic nature of eachindividual tumour have resulted in all therapeutic clinical trials failing during the past 20 years. Recently, immunotherapy has beenshowing great promise, but only in subsets of patients. Identifying those patients cannot be done a priori as biomarkers are still largelymissing, nor are we able to follow-up on therapeutic efficacy when patients get treated. The GLIOMATCH project aims at improvingthe clinical outcome of GBM/pHGG patients by enabling immunology-based patient stratification to empower personalised matchingof appropriate immunotherapy, while improving follow-up of clinical responses to existing/novel therapeutics. This will be achievedby integrating spatially resolved, multi-layered tissue maps (using integrated single-cell multiomics), with non-invasive MRI images.This integration will fuel into a novel MRI Radio-multiomics hub, that will be made available to clinical professionals through whichthey can perform tumour-host based patient stratification and personalised therapy matching while interpreting longitudinal follow-upand treatment efficacy. The proposed data-driven models will be developed by analysing the largest cohort of immuno-oncology (I/O)treated GBM/pHGG patients (n>300, including pre-post treatment samples) with matched controls (n>300) and exceptionally long-termsurviving GBM patients (n~140), in which various tumour-host niches will be studied in how they respond to I/O perturbations andlead to improved clinical outcome. This will be empowered by deploying an UNCAN-compatible data lake, to which incremental datacollection will be used to further refine the machine learning models, while proposing novel treatment options. This action is part of theCancer Mission cluster of projects on “Understanding (tumour-host interactions)".
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